Concept

Mandelbrot set

Related notebooks (14)

Interactive Phonon Band Structure Visualization

In [1]: import numpy as np import matplotlib.pyplot as plt from matplotlib import animation from matplotlib import rc import ipywidgets as ipw from ipywidgets import interact,interactive import ipywidgets as widgets from IPython.display import HTML %matplotlib widget global global_K1 global global_K2 global global_branche global global_selected_k In [2]: def get_closest_point(clic_x,clic_y): # Get the data from the figure ax= plt.gca() # get axis handle acoustic_data = ax.lines[1] op

Illustrating Band Structures in Solids: Free Electrons and Atomic Orbitals

In [1]: import numpy as np from numpy import linalg as LA import matplotlib.pyplot as plt

from future import print_function

from ipywidgets import interact, interactive, fixed, interact_manual import ipywidgets as widgets %matplotlib widget The emergence of band structures[1]¶ If you have questions about this notebook, e-mail Oleg Malanyuk (oleg.malanyuk@epfl.ch) This jupyter notebook is intended to provide a illustration of the band structures in solides, starting from the points of v

Understanding Hund s Rules

numbers

S

L

and

J

of an isolated atom or ion from its electronic configuration. Considering a partially filled electronic shell, the following three rules have to be applied in the order given below: 1. The total spin angular momentum

S=\Sigma|m_s|

takes its maximum possible value; 2. The total orbital angular momentum

L=\Sigma|m_l|

is maximized; 3. For more than half-filled shell, the total angular momentum number

J=L+S

, otherwise

J=\vert L-S \vert

. The sl

Exploring the Karplus-Strong Algorithm for Sound Synthesis

The Karplus-Strong Algorithm[1]¶

                                      [2]Paolo Prandoni, [3]LCAV, EPFL

The Karplus-Strong algorithm is a simple method to generate synthetic sounds with arbitrary pitch. In this notebook we will explore a few variations of the method to illustrate some basic notions of discrete-time signal processing. In [1]: %matplotlib inline import matplotlib import matplotlib.pyplot as plt import numpy as np import IPython In [2]: plt.rcParams["figure.figsize"]

AC Conductivity and Permittivity Calculations Using the Drude Model

Chapter 1: AC Conductivity and Permittivity¶{.anchor-link} {#Chapter-1:-AC-Conductivity-and-Permittivity}

::: ::: ::: ::: ::: {.jp-Cell .jp-MarkdownCell .jp-Notebook-cell} ::: {.jp-Cell-inputWrapper tabindex="0"} ::: {.jp-Collapser .jp-InputCollapser .jp-Cell-inputCollapser} ::: ::: {.jp-InputArea .jp-Cell-inputArea} ::: {.jp-InputPrompt .jp-InputArea-prompt} ::: ::: {.jp-RenderedHTMLCommon .jp-RenderedMarkdown .jp-MarkdownOutput mime-type="text/markdown"

Understanding the Haar Basis for Grayscale Image Compression

A Basis for Grayscale Images[1]¶

                                      [2]Paolo Prandoni, [3]LCAV, EPFL

The concept of "basis" for a vector space is one of the most profound and fruitful ideas in linear algebra. Basis vectors are the "building blocks" that all other vectors are built from; by "disassembling" a vector into a linear combination of basis elements we are able to "look inside" the vector and highlight many of its relevant features. A vector space has an infinite number o

Band Structure Investigation of Monolayer and Bilayer Graphene Using the Tight Binding Model

In [1]: import matplotlib.pyplot as plt import numpy as np from mpl_toolkits import mplot3d import itertools %matplotlib widget Band Structure of Mono and Bilayer Graphene[1]¶ If you have questions about this notebook, e-mail Oleg Malanyuk (oleg.malanyuk@epfl.ch) The purpose of this notebook is to investigate the band model of Mono and Bilayer graphene modeled with tight binding model. Tight binding model for monolayer graphene[2]¶ Monolayer graphene is a sheet of carbons aranged in a he

Cylindrical Coordinates Visualization

::: {role="main"} ::: {.jp-Cell .jp-MarkdownCell .jp-Notebook-cell} ::: {.jp-Cell-inputWrapper tabindex="0"} ::: {.jp-Collapser .jp-InputCollapser .jp-Cell-inputCollapser} ::: ::: {.jp-InputArea .jp-Cell-inputArea} ::: {.jp-InputPrompt .jp-InputArea-prompt} ::: ::: {.jp-RenderedHTMLCommon .jp-RenderedMarkdown .jp-MarkdownOutput mime-type="text/markdown"}

Important note¶{.anchor-link} {#Important-note}

This notebook is obsolete and not maintained. For an interactive display of cylidri

Estimation of Electron Density and Mean Free Time for Transparent Conductors

Chapter 1: Transparent Conductors[1]¶ Instruction: The purpose of this notebook is to try and find values of electron density and mean free time for a material suitable to be used as a transparent conductors. The final estimate should be the order of the two parameters to be used[2]¶ In [1]: import numpy as np from bokeh.io import push_notebook, output_notebook, show from bokeh.layouts import row, column from bokeh.plotting import figure output_notebook() from ipywidgets import interact from collect

Exploring Frequency Beatings and Intermodulation Effects with Sinusoids

Feel the Beat(s)[1]¶

                                      [2]Paolo Prandoni, [3]LCAV, EPFL

This simple notebook lets you interactively explore the intermodulation effects of two superimposed sinusoids. In [1]:

standard bookkeeping

%matplotlib inline import matplotlib.pyplot as plt import numpy as np from IPython.display import Audio, display

interactivity library:

from ipywidgets import interact, interactive, fixed, interact_manual import ipywidgets as widgets In [2]: plt.rcParams[

Whistle-Activated Keylock System Implementation

Whistling Keylock[1]¶ In this notebook we will implement a simple "whistle-activated" keylock, that is, an access control system where the password is a pre-defined melody. To program the lock, you record your musical passphrase and then, to open the lock, you whistle it back. Although the implementation will be simple and skip most of the fine details that would be necessary to produce a truly robust system, we need to take into account at least the following points: * The syste

Simulating a Telephone Channel using FIR Filter Design

A Telephone Channel Simulator[1]¶ In this notebook we will develop a filter that simulates the effect of a standard telephone channel. In [1]: %matplotlib inline import matplotlib.pyplot as plt import numpy as np import IPython import scipy.signal as sp from scipy.io import wavfile In [2]: plt.rcParams["figure.figsize"] = (14,4) implicit sampling rate will be the internal "clock" of our simulation. In [3]: IPython.display.Audio(s, rate=SF) Out[3]: Your browser does not support

Digital Simulation of Guitar Feedback in I Feel Fine by The Beatles

I Feel Fine, Digitally[1]¶ This notebook is a little musing inspired by the "internal mechanics" of a very famous audio snippet, namely the guitar sound at the beginning of the song "I Feel Fine", written and recorded by the Beatles in 1964. The historical significance of the record lies in the following claim by John Lennon:

 "I defy anybody to find a record... unless it is some old blues
 record from 1922... that uses feedback that way. So I claim it for
 the Beatles. Before H

Adaptive Echo Cancellation using LMS Filter

Adaptive Echo Cancellation[1]¶

                                      [2]Paolo Prandoni, [3]LCAV, EPFL

In this notebook we will use the adaptive LMS filter to estimate the impulse response of a reverberating room. A typical use case is handsfree telephony: since the received signal, amplified and played by the speaker, is picked up by the microphone, we need to eliminate it from the signal that is transmitted back in order to avoid a feedback loop. The signal picked up by the microph

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